Dataset Information

In-depth resistome analysis by targeted metagenomics.

ABSTRACT: BACKGROUND:Antimicrobial resistance is a major global health challenge. Metagenomics allows analyzing the presence and dynamics of "resistomes" (the ensemble of genes encoding antimicrobial resistance in a given microbiome) in disparate microbial ecosystems. However, the low sensitivity and specificity of available metagenomic methods preclude the detection of minority populations (often present below their detection threshold) and/or the identification of allelic variants that differ in the resulting phenotype. Here, we describe a novel strategy that combines targeted metagenomics using last generation in-solution capture platforms, with novel bioinformatics tools to establish a standardized framework that allows both quantitative and qualitative analyses of resistomes. METHODS:We developed ResCap, a targeted sequence capture platform based on SeqCapEZ (NimbleGene) technology, which includes probes for 8667 canonical resistance genes (7963 antibiotic resistance genes and 704 genes conferring resistance to metals or biocides), and 2517 relaxase genes (plasmid markers) and 78,600 genes homologous to the previous identified targets (47,806 for antibiotics and 30,794 for biocides or metals). Its performance was compared with metagenomic shotgun sequencing (MSS) for 17 fecal samples (9 humans, 8 swine). ResCap significantly improves MSS to detect "gene abundance" (from 2.0 to 83.2%) and "gene diversity" (26 versus 14.9 genes unequivocally detected per sample per million of reads; the number of reads unequivocally mapped increasing up to 300-fold by using ResCap), which were calculated using novel bioinformatic tools. ResCap also facilitated the analysis of novel genes potentially involved in the resistance to antibiotics, metals, biocides, or any combination thereof. CONCLUSIONS:ResCap, the first targeted sequence capture, specifically developed to analyze resistomes, greatly enhances the sensitivity and specificity of available metagenomic methods and offers the possibility to analyze genes related to the selection and transfer of antimicrobial resistance (biocides, heavy metals, plasmids). The model opens the possibility to study other complex microbial systems in which minority populations play a relevant role.

SUBMITTER: Lanza VF

PROVIDER: S-EPMC5769438 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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In-depth resistome analysis by targeted metagenomics.

Lanza Val F VF Baquero Fernando F Martínez José Luís JL Ramos-Ruíz Ricardo R González-Zorn Bruno B Andremont Antoine A Sánchez-Valenzuela Antonio A Ehrlich Stanislav Dusko SD Kennedy Sean S Ruppé Etienne E van Schaik Willem W Willems Rob J RJ de la Cruz Fernando F Coque Teresa M TM

Microbiome 20180115 1

<h4>Background</h4>Antimicrobial resistance is a major global health challenge. Metagenomics allows analyzing the presence and dynamics of "resistomes" (the ensemble of genes encoding antimicrobial resistance in a given microbiome) in disparate microbial ecosystems. However, the low sensitivity and specificity of available metagenomic methods preclude the detection of minority populations (often present below their detection threshold) and/or the identification of allelic variants that differ in ...[more]

PMID: 29335005

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Project description:Antibiotic resistance genes (ARGs) pose a serious threat to public health and ecological security in the 21st century. However, the resistome only accounts for a tiny fraction of metagenomic content, which makes it difficult to investigate low-abundance ARGs in various environmental settings. Thus, a highly sensitive, accurate, and comprehensive method is needed to describe ARG profiles in complex metagenomic samples. In this study, we established a high-throughput sequencing method based on targeted amplification, which could simultaneously detect ARGs (n = 251), mobile genetic element genes (n = 8), and metal resistance genes (n = 19) in metagenomes. The performance of amplicon sequencing was compared with traditional metagenomic shotgun sequencing (MetaSeq). A total of 1421 primer pairs were designed, achieving extremely high coverage of target genes. The amplicon sequencing significantly improved the recovery of target ARGs (~9 × 104-fold), with higher sensitivity and diversity, less cost, and computation burden. Furthermore, targeted enrichment allows deep scanning of single nucleotide polymorphisms (SNPs), and elevated SNPs detection was shown in this study. We further performed this approach for 48 environmental samples (37 feces, 20 soils, and 7 sewage) and 16 clinical samples. All samples tested in this study showed high diversity and recovery of targeted genes. Our results demonstrated that the approach could be applied to various metagenomic samples and served as an efficient tool in the surveillance and evolution assessment of ARGs. Access to the resistome using the enrichment method validated in this study enabled the capture of low-abundance resistomes while being less costly and time-consuming, which can greatly advance our understanding of local and global resistome dynamics. IMPORTANCE ARGs, an increasing global threat to human health, can be transferred into health-related microorganisms in the environment by horizontal gene transfer, posing a serious threat to public health. Advancing profiling methods are needed for monitoring and predicting the potential risks of ARGs in metagenomes. Our study described a customized amplicon sequencing assay that could enable a high-throughput, targeted, in-depth analysis of ARGs and detect a low-abundance portion of resistomes. This method could serve as an efficient tool to assess the variation and evolution of specific ARGs in the clinical and natural environment.

Project description:Food waste diversion and composting, either mandated or voluntary, are growing alternatives to traditional waste disposal. An acceptable source of agricultural feed and composting material, methane-emitting food residuals, including post-consumer food scraps, are diverted from landfills allowing recapture of nutrients that would otherwise be lost. However, risk associated with the transfer of antimicrobial resistant bacteria (ARB), antibiotic resistance genes (ARGs), or pathogens from food waste is not well characterized. Using shotgun metagenomic sequencing, ARGs, microbial content, and associated virulence factors were successfully identified across samples from an integrated poultry farm that feeds post-consumer food waste. A total of 495 distinct bacterial species or sub-species, 50 ARGs, and 54 virulence gene sequences were found. ARG sequences related to aminoglycoside, tetracycline, and macrolide resistance were most prominent, while most virulence gene sequences were related to transposon or integron activity. Microbiome content was distinct between on-farm samples and off-farm food waste collection sites, with a reduction in pathogens throughout the composting process. While most samples contained some level of resistance, only 3 resistance gene sequences occurred in both on- and off-farm samples and no multidrug resistance (MDR) gene sequences persisted once on the farm. The risk of incorporating novel or multi-drug resistance from human sources appears to be minimal and the practice of utilizing post-consumer food scraps as feed for poultry and composting material may not present a significant risk for human or animal health. Pearson correlation and co-inertia analysis identified a significant interaction between resistance and virulence genes (P = 0.05, RV = 0.67), indicating that ability to undergo gene transfer may be a better marker for ARG risk than presence of specific bacterial species. This work expands the knowledge of ARG fate during food scrap animal feeding and composting and provides a methodology for reproducible analysis.

Project description:BackgroundThe gut microbiome of domestic animals carries antibiotic resistance genes (ARGs) which can be transmitted to the environment and humans, resulting in challenges of antibiotic resistance. Although it has been reported that the rumen microbiome of ruminants may be a reservoir of ARGs, the factors affecting the temporal dynamics of the rumen resistome are still unclear. Here, we collected rumen content samples of goats at 1, 7, 14, 28, 42, 56, 70, and 84 days of age, analyzed their microbiome and resistome profiles using metagenomics, and assessed the temporal dynamics of the rumen resistome in goats at the early stage of life under a conventional feeding system.ResultsIn our results, the rumen resistome of goat kids contained ARGs to 41 classes, and the richness of ARGs decreased with age. Four antibiotic compound types of ARGs, including drugs, biocides, metals, and multi-compounds, were found during milk feeding, while only drug types of ARGs were observed after supplementation with starter feed. The specific ARGs for each age and their temporal dynamics were characterized, and the network inference model revealed that the interactions among ARGs were related to age. A strong correlation between the profiles of rumen resistome and microbiome was found using Procrustes analysis. Ruminal Escherichia coli within Proteobacteria phylum was the main carrier of ARGs in goats consuming colostrum, while Prevotella ruminicola and Fibrobacter succinogenes associated with cellulose degradation were the carriers of ARGs after starter supplementation. Milk consumption was likely a source of rumen ARGs, and the changes in the rumen resistome with age were correlated with the microbiome modulation by starter supplementation.ConclusionsOur data revealed that the temporal dynamics of the rumen resistome are associated with the microbiome, and the reservoir of ARGs in the rumen during early life is likely related to age and diet. It may be a feasible strategy to reduce the rumen and its downstream dissemination of ARGs in ruminants through early-life dietary intervention. Video Abstract.

Project description:Metagenomics can unveil the genetic content of the total microbiota in different environments, such as food products and the guts of humans and livestock. It is therefore considered of great potential to investigate the transmission of foodborne hazards as part of source-attribution studies. Source-attribution of antimicrobial resistance (AMR) has traditionally relied on pathogen isolation, while metagenomics allows investigating the full span of AMR determinants. In this study, we hypothesized that the relative abundance of fecal resistome components can be associated with specific reservoirs, and that resistomes can be used for AMR source-attribution. We used shotgun-sequences from fecal samples of pigs, broilers, turkeys- and veal calves collected across Europe, and fecal samples from humans occupationally exposed to livestock in one country (pig slaughterhouse workers, pig and broiler farmers). We applied both hierarchical and flat forms of the supervised classification ensemble algorithm Random Forests to classify resistomes into corresponding reservoir classes. We identified country-specific and -independent AMR determinants, and assessed the impact of country-specific determinants when attributing AMR resistance in humans. Additionally, we performed a similarity percentage analysis with the full spectrum of AMR determinants to identify resistome signatures for the different reservoirs. We showed that the number of AMR determinants necessary to attribute a resistome into the correct reservoir increases with a larger reservoir heterogeneity, and that the impact of country-specific resistome signatures on prediction varies between countries. We predicted a higher occupational exposure to AMR determinants among workers exposed to pigs than among those exposed to broilers. Additionally, results suggested that AMR exposure on pig farms was higher than in pig slaughterhouses. Human resistomes were more similar to pig and veal calves' resistomes than to those of broilers and turkeys, and the majority of these resistome dissimilarities can be explained by a small set of AMR determinants. We identified resistome signatures for each individual reservoir, which include AMR determinants significantly associated with on-farm antimicrobial use. We attributed human resistomes to different livestock reservoirs using Random Forests, which allowed identifying pigs as a potential source of AMR in humans. This study thus demonstrates that it is possible to apply metagenomics in AMR source-attribution.

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In-depth resistome analysis by targeted metagenomics.

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In-depth resistome analysis by targeted metagenomics.

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